Estimating regional species richness of tropical insects from museum data: a comparison of a geography‐based and sample‐based methods

Abstract

Summary The identification of spatial patterns of species richness at regional scales, such as biodiversity hotspots, is complicated in invertebrate taxa (particularly those from tropical regions) by incomplete and biased inventory data. Estimation techniques of regional species richness from incompletely sampled landscapes have recently become available, but their applicability to data from museum collections and local taxonomic checklists has not been investigated. We use records of sphingid moths in grid cells of 1° latitude and longitude on 14 Malesian islands to estimate the total species richness of these islands, as well as that of the whole archipelago, by five parametric and nonparametric estimation techniques. We compare these values to figures based on GIS‐supported estimates of geographical ranges of each species. Our analyses suggest that the F3 estimator of regional species richness leads to least deviation from the GIS‐based estimate, followed by Chao2 and ICE, which are often employed in local comparisons. Overestimation occurred more often than underestimation and estimates of well‐sampled islands (with many available grid cells) are less deviant than those of poorly sampled islands. We did not obtain conclusive results as to whether strongly undersampled grid cells are better excluded from an analysis (at the cost of reduced grid cell number) or not. Synthesis and applications. In agreement with some previously published assessments, we conclude that the F3 estimator has the greatest potential for predicting regional species richness in partially sampled landscapes. Sample‐based methods for estimating regional species richness can provide an alternative to the much more work‐intensive geographical modelling of species distributions, which may facilitate the inclusion of tropical invertebrate groups in documenting global diversity patterns. However, under conditions of incomplete, non‐systematic sampling, which is typical for museum and checklist data, errors can still be large, particularly if the number of sampling units (e.g. grid cells) is low. Estimation values should not be interpreted uncritically when the data conditions that lead to biased values are not precisely defined.